Machines produce operation data. Sensors monitor the equipment. Software monitors production performance. These are the current realities in manufacturing. However, data gathering is just a first step.
The next step is implementing the use of artificial intelligence (AI) to make the use of data more effective, making sure that humans remain engaged in the process of decision-making.
It seems like human-in-the-loop AI could become an important paradigm of modern industrial automation.
Rule-Based Automation vs. AI-Assisted Operations
Automation of industrial processes involves following predefined rules.
When a certain condition is detected, then predefined action takes place.
AI involves a different logic. Machine learning algorithms are capable of analyzing historic and current data, detecting patterns, identifying anomalies, and making predictions.
In case of monitoring industrial equipment via AI, for example, the system will detect unusual vibrations or temperatures. However, the system won't shut the equipment automatically. It will notify the engineer of the issue and give him/her a chance to look into it.
AI works with the data. The human is responsible for the interpretation of the situation.
Quality Control With the Aid of Artificial Intelligence
The use of computer vision is yet another example of AI-human interaction.
Images of products traveling along the production line can be captured using cameras, whereas machine learning algorithms will be used to inspect these pictures for potential defects.
Suspicious items can be identified automatically and sent for further inspection by humans.
It is a good example of a division of roles between AI and humans:
- AI performs the bulk of the work with images.
- Humans deal with questionable cases.
- Engineers analyze defective item patterns.
- Production staff applies their findings to optimize the process.
Why Is Data Infrastructure So Important?
Artificial intelligence solutions rely greatly on data available in industry.
Legacy devices, unconnected software applications, and fragmented databases are quite common in many plants. The process of creating a link between all these components can prove to be harder than building an AI algorithm.
Therefore, a successful installation could potentially require:
- Sufficiently good data from sensors.
- Data pipelines.
- Common data format.
- Real-time monitoring.
- Safe infrastructure.
- Model evaluation and monitoring.
It is impossible to achieve reliable results without a proper data foundation even for advanced AI algorithms.
Human Influence
The use of AI shouldn't be perceived as an autonomous substitution of expertise in manufacturing.
Engineers and operators have knowledge of the production conditions that are not represented in the datasets. They can identify anomalies, analyze recommendations, and make decisions based on incomplete data.
Firms like PowderForge AI have developed AI-based solutions for contemporary industrial enterprises, adding one more piece to the trend of intelligent and data-driven manufacturing.
The Way Forward
Future industrial automation will probably include both traditional automation, AI, and human knowledge and experience.
AI can process immense amounts of data and recognize patterns. Humans can bring context, creativity, experience, and responsibility into the process.
The consequence of this combination is not a more automated factory but an intelligent and data-driven manufacturing environment that uses the skills of both intelligent systems and people.
The point is not about humanless automation. The goal is about making intelligent systems enhance the decisions of humans.
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